A general face recognition method extracts feature data of a face and compares the extracted feature data with pre-stored data to determine similarity between the extracted feature data and the pre-stored data. The similarity between the extracted feature data of the face and the pre-stored data may be determined using geometrical features of certain parts of the face such as for example the position of an eye, and the position of a mouth, or by using features such the size of an eye, the size of a mouth, and the histogram of an eye etc.
However, there may be cases where it is not possible to identify the feature data of a face or the feature data being provided is less than a critical value. For example, in the case of using feature data of an eye, if a portion or an entirety of the eye is blocked by hair or glasses etc., it would be either not possible to obtain any feature data of the eye, or the feature data being provided may be less than the critical value, making it difficult to determine the similarity between the feature data and the pre-stored data. Likewise, in the case of using feature data of a mouth, if a portion or an entirety of the mouth is blocked by a hand or clothes etc., it would be either not possible to obtain any feature data of the mouth, or the feature data being provided may be less than a critical value, making it difficult to determine the similarity between the feature data and the pre-stored data.
Consequently, in the case where some of the feature data is not identifiable from the image or the feature data being provided is less than the critical value, a recognition error may occur or it may be difficult to recognize the face even when the feature data of a same person exists in the database.